lsst.gauss2d¶
gauss2d provides classes and methods for defining 2D Gaussian mixtures and evaluating their (approximate) integrals over square pixels. Several ellipse parameterizations are implemented but evaluations are done strictly with the (sigma_x, sigma_y, rho) variant, since this keeps rho bounded between 1 and 1 and not periodic (like a position angle would be).
gauss2d can also evaluate the first derivatives of a model (i.e. the Jacobian) or its likelihood analytically.
Using lsst.gauss2d¶
Example usage can be found in the unit tests and also in dependent packages, particularly gauss2d_fit.
Contributing¶
lsst.gauss2d
is developed at https://github.com/lsstdm/gauss2d.
You can find Jira issues for this module under the
gauss2d
component.
Python API reference¶
lsst.gauss2d
has Python bindings for classes using numpybased single
and double precision arrays. Support for GSL arrays is forthcoming with
DM38617.
lsst.gauss2d Package¶
Functions¶

Evaluate a 2D Gaussian at the centers of pixels on a rectangular grid using the standard bivariateGaussian PDF. 

Evaluate a 2D Gaussian at the centers of pixels on a rectangular grid using the standard bivariateGaussian PDF. 